Ebook:
Asset Reliability: From Hiccuping to Humming Production Floors

Digitization has placed immense pressure on businesses and the way they function. Organizations are struggling to cope with not only increasingly sophisticated machines, but also the data these machines generate.

To make things worse, persistently trying economic conditions have led to a recurring cycle of unrealistic challenges, the most harrowing of which is unplanned asset downtime. However, in today’s times of relentless, breakneck-paced growth, there’s zero scope for asset breakdown and downtime. How then do you get your machines to cooperate and not break down in the asset-intensive manufacturing industry, where machine failure and downtime are annoyingly common occurrences?

Cognitive predictive maintenance may be the answer to that question. To learn more, check out our latest ebook, which covers how cognitive predictive maintenance (CPdM) can:

Help prevent asset failure

Achieve 100% asset reliability

Break the cycle of typical manufacturing challenges

Download it today and put your asset reliability woes to rest.

About DataRPM

DataRPM is an award-winning predictive analytics company focused on delivering the next generation predictive maintenance solutions for the Industrial IoT. DataRPM platform automates data science leveraging the next frontier in machine learning known as meta-learning, which is machine learning on machine learning. The platform increases prediction quality and accuracy by over 300% in 1/30th the time and resources delivering 30% in cost savings or revenue growth for business problems around predicting asset failures, reducing maintenance costs, optimizing inventory and resources, predicting quality issues, forecasting warranty and insurance claims and managing risks better.

Progress, Telerik, and certain product names used herein are trademarks or registered trademarks of Progress Software Corporation and/or one of its subsidiaries or affiliates in the U.S. and/or other countries. See Trademarks for appropriate markings.